Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: A case study in Virginia and North Carolina, USA

Abstract Large-scale, accurate and detailed forest height map is worthwhile and necessary for understanding and assessing global carbon cycle and biodiversity. The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission, employing a photon-counting LiDAR (PCL) system, offers an opportunity to map global forest height with high resolution. This study aimed to map forest height at a spatial resolution of 30 m by combining Multiple Altimeter Beam Experimental Lidar (MABEL) data with Landsat 8 Operational Land Imager (OLI) data. There are four key steps to accomplish this goal. First, a segmentation method based on the Douglas-Peucker algorithm was proposed to solve the problem of large turns or calibration maneuvers in MABEL data. Second, we estimated forest height and selected the forest height samples with high accuracy and reliability by developing three filters including signal-to-noise ratio (SNR) filter, slope filter, and canopy photons density (CPD) filter. Third, forest height models based on both random forest (RF) and stepwise multiple regression algorithms were developed to establish relationships between the selected forest height samples and predicator variables of Landsat-derived spectral indices, topographic variables and geographic coordinates. Finally, a wall-to-wall forest height map was generated by applying the developed forest height models to predicator variables, and the accuracy of forest height map was validated using airborne LiDAR-derived forest heights. An area of 160, 000 km2 in southeast Virginia and east North Carolina was chosen for testing the methods proposed in this study. The results demonstrated that the Douglas-Peucker algorithm can effectively solve the MABEL data overlapping issues caused by large turns and calibration maneuvers in flight lines. The suitable filters for selecting forest heights are SNR > 6, terrain slope